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Data startup: Outside Intelligence

Here at Data Catalyst, we are strong supporters of data exploration, utilization and innovation. That’s why, every few months, we profile an organization that is using data as a core part of their business.

Startups in the innovation economy are prime examples of companies using data in more and more interesting ways to deliver novel services and products. This month, Outside Intelligence shares some of the philosophy behind their product.

Tell us a little bit about your company and your product/service.

OutsideIQ is working with several world-class financial institutions to build applications that discover and crunch through data to better understand and price risk. Our Risk Discovery Platform™ mines data using contextual analytics to determine relevant attributes for the creation of accurate risk models.

Tell us a little bit about you—the people who make up your company.

We’re actually a very diverse group. Dan Adamson, our CEO, has a background in search and analytics, working at Microsoft on Bing and before that at Medstory, a vertical search company in health. Many of us have degrees in physical sciences, math, actuarial sciences and, of course, computer science. That said, computer science is probably in the minority. It makes for a fun, eclectic group and it’s awesome to see us take a multi-discipline approach and work so well together.

At the end of the day, regardless of our backgrounds, everyone is very passionate about what we’re trying to do.

Explain the intersection of data and the work that your company does. How is data important to your work?

Data forms the very foundation of what we do. Traditional risk modelling focuses on analyzing a very small subset of well-curated data. The reality is that if you focus only on that well-controlled data, a lot of important risk factors get missed. We have a different way of thinking about data. We assume all data will be noisy, and it’s our goal to let the machines learn and strip out all of that noise until we are only left with clear signals. Under that approach, the more data we can analyze the better, and we don’t have to focus on only the data that has been well curated.

What are you doing with data that others may not be doing? What kind of impact are you having on Ontario’s (and Canada’s) data landscape?

We’re at the early stages, but we’re starting to change the way people think about data. Traditionally, the truth has been pretty simple—if you gave me a choice between good algorithms and good data, I would choose good data every time. So the focus has been on clean data and selecting a few attributes. Our technology is pretty unique in that it is trained to uncover that good data. We’re already making an impact with several leading insurance carriers and banks in Canada. They are already well respected for their ability to avoid risk, and now they’re pricing risks more accurately, recognizing potential risks sooner and generally improving both their top and bottom lines.

What are your thoughts about the emergence of data as an important part of decision-making?

In a lot of cases, we see that the “emergence of data” might now just be the fashionable way of making work for IT departments. The data deluge was a well-known problem long before Big Data became the latest IT buzzword. If we could give one piece of advice to those looking to experiment with Big Data, it would be to stay focused on proving out an ROI. Until you can prove out the ROI, Big Data is just more noise and, as such, can be counter to better decision-making.

Where can we learn a bit more about what you do and your work with data?

We have several white papers and more information about the technology on our website. If you’re talented and passionate about working on some really tough problems, please apply for a position and learn more that way!

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